Recent years have seen significant improvement in digital systems that provide dynamic web content to client devices across computer networks. Indeed, it is now common for websites or other digital assets hosted at remote servers to include personalized and adaptive content tailored to individual users of client devices. For example, conventional digital content testing and distribution systems can provide uniquely tailored lists of images, videos, documents, or other digital content to individual client devices by applying digital content selection policies.
In an effort to implement effective digital content selection policies, conventional digital content testing and distribution systems often perform testing between content selection policies to identify whether a particular content selection policy is effective or not. While conventional digital content testing and distribution systems can evaluate and deliver lists of digital content, these systems still include a variety of problems and drawbacks.
For example, conventional systems are often inaccurate. To illustrate, conventional digital content testing and distribution systems often evaluate effectiveness of content selection policies by conducting an online comparison test (e.g., an online bucket test such as A/B testing) and tracking key metrics such as clicks, downloads, and purchases to determine which of two different policies perform better. While online comparison tests provide a tool for comparing two digital content selection policies, the digital content policies utilized in such tests are often inaccurate and provide erroneous digital content to individual client devices while testing (e.g., because online comparison tests often analyze less effective digital content selection policies that yield unknown results). Accordingly, these systems often result in a poor viewer experience, particularly for those viewers who receive erroneous digital content in accordance with a less effective policy. Indeed, many viewers who receive digital content in accordance with less effective content selection policies will navigate to other websites.
In addition, conducting online comparison tests is often expensive, inefficient, and inflexible. For example, conducting an online comparison test often involves distributing (and tracking distribution) of thousands of digital content lists across any number of client devices in addition to tracking and analyzing interactions with the distributed content to determine an effectiveness of a given content selection policy. These tests, thus, require significant computing resources in applying digital content policies in response to detecting opportunities corresponding to various client devices and distributing digital content to those client devices. In addition, because viewer behavior and individual online activity change over time, content distributors cannot replicate online comparison tests to confirm testing results. Accordingly, conventional systems generally utilize rigid testing approaches that apply only to specific digital content policies.
As an alternative to online comparison testing, some conventional systems perform policy evaluation by predicting user interactions with respective lists of digital content across a distribution of lists associated with corresponding content selection policies. As lists increase in length and content variety, however, the number of possible digital content lists that a content distributor can provide to target users grows exponentially. As a result, conventional policy evaluation techniques become computationally prohibitive as the number of combinations of different digital content items in digital content lists increases (and the amount of data necessary to accurately evaluate item lists for target policies increases).
These and other problems exist with regard to evaluating distribution policies for providing lists of digital content items.